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* Author: Christoph Rösmann
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#include <costmap_converter/costmap_to_lines_ransac.h>
#include <boost/thread.hpp>
#include <boost/thread/mutex.hpp>
#include <pluginlib/class_list_macros.h>

PLUGINLIB_EXPORT_CLASS(costmap_converter::CostmapToLinesDBSRANSAC, costmap_converter::BaseCostmapToPolygons)

namespace costmap_converter
{

CostmapToLinesDBSRANSAC::CostmapToLinesDBSRANSAC() : CostmapToPolygonsDBSMCCH()
{
    dynamic_recfg_ = NULL;
}

CostmapToLinesDBSRANSAC::~CostmapToLinesDBSRANSAC() { delete dynamic_recfg_; }

void CostmapToLinesDBSRANSAC::initialize(ros::NodeHandle nh)
{
    // DB SCAN
    nh.param("cluster_max_distance", parameter_.max_distance_, 0.4);
    nh.param("cluster_min_pts", parameter_.min_pts_, 2);
    nh.param("cluster_max_pts", parameter_.max_pts_, 30);
    // convex hull (only necessary if outlier filtering is enabled)
    nh.param("convex_hull_min_pt_separation", parameter_.min_keypoint_separation_, 0.1);
    parameter_buffered_ = parameter_;
    // ransac
    nh.param("ransac_inlier_distance", ransac_inlier_distance_, 0.2);
    nh.param("ransac_min_inliers", ransac_min_inliers_, 10);
    nh.param("ransac_no_iterations", ransac_no_iterations_, 2000);
    nh.param("ransac_remainig_outliers", ransac_remainig_outliers_, 3);
    nh.param("ransac_convert_outlier_pts", ransac_convert_outlier_pts_, true);
    nh.param("ransac_filter_remaining_outlier_pts", ransac_filter_remaining_outlier_pts_, false);
    // setup dynamic reconfigure
    dynamic_recfg_ = new dynamic_reconfigure::Server<CostmapToLinesDBSRANSACConfig>(nh);
    dynamic_reconfigure::Server<CostmapToLinesDBSRANSACConfig>::CallbackType cb = boost::bind(
            &CostmapToLinesDBSRANSAC::reconfigureCB, this, _1, _2);
    dynamic_recfg_->setCallback(cb);
}

void CostmapToLinesDBSRANSAC::compute()
{
    std::vector<std::vector<KeyPoint> > clusters;
    dbScan(clusters);
    // Create new polygon container
    PolygonContainerPtr polygons(new std::vector<geometry_msgs::Polygon>());
    // fit lines using ransac for each cluster skip first cluster, since it is just noise
    for (int i = 1; i < clusters.size(); ++i)
    {
        while (clusters[i].size() > ransac_remainig_outliers_)
        {
            std::vector<KeyPoint> outliers;
            std::pair<KeyPoint, KeyPoint> model;
            if (!lineRansac(clusters[i], ransac_inlier_distance_, ransac_no_iterations_,
                            ransac_min_inliers_, model, NULL, &outliers)) break;
            // add to polygon container
            geometry_msgs::Polygon line;
            line.points.resize(2);
            model.first.toPointMsg(line.points.front());
            model.second.toPointMsg(line.points.back());
            polygons->push_back(line);
            clusters[i] = outliers;
        }
        // create point polygons for remaining outliers 为剩余的离群值创建点多边形
        if (ransac_convert_outlier_pts_)
        {
            if (ransac_filter_remaining_outlier_pts_)
            {
                // take edge points of a convex polygon
                // these points define a cluster and since all lines are extracted,
                // we remove points from the interior...
                geometry_msgs::Polygon polygon;
                convexHull2(clusters[i], polygon);
                for (auto point : polygon.points)
                {
                    polygons->push_back(geometry_msgs::Polygon());
                    convertPointToPolygon(point, polygons->back());
                }
            }
            else
            {
                for (auto j : clusters[i])
                {
                    polygons->push_back(geometry_msgs::Polygon());
                    convertPointToPolygon(j, polygons->back());
                }
            }
        }
    }
    // add our non-cluster points to the polygon container (as single points)
    if (!clusters.empty())
    {
        for (auto i : clusters.front())
        {
            polygons->push_back(geometry_msgs::Polygon());
            convertPointToPolygon(i, polygons->back());
        }
    }
    // replace shared polygon container
    updatePolygonContainer(polygons);
}


bool CostmapToLinesDBSRANSAC::lineRansac(const std::vector<KeyPoint> &data, double inlier_distance,
        int no_iterations, int min_inliers, std::pair<KeyPoint, KeyPoint> &best_model,
        std::vector<KeyPoint> *inliers, std::vector<KeyPoint> *outliers)
{
    if (data.size() < 2 || data.size() < min_inliers) return false;
    boost::random::uniform_int_distribution<> distribution(0, data.size() - 1);
    std::pair<int, int> best_model_idx;
    int best_no_inliers = -1;
    for (int i = 0; i < no_iterations; ++i)
    {
        // choose random points to define a line candidate
        // 选择一个随机点来定义一个候选线
        int start_idx = distribution(rnd_generator_);
        int end_idx = start_idx;
        while (end_idx == start_idx) end_idx = distribution(rnd_generator_);
        // compute inliers 计算窗
        int no_inliers = 0;
        for (int j = 0; j < (int) data.size(); ++j)
        {
            if (isInlier(data[j], data[start_idx], data[end_idx], inlier_distance)) ++no_inliers;
        }
        if (no_inliers > best_no_inliers)
        {
            best_no_inliers = no_inliers;
            best_model_idx.first = start_idx;
            best_model_idx.second = end_idx;
        }
    }
    best_model.first = data[best_model_idx.first];
    best_model.second = data[best_model_idx.second];
    if (best_no_inliers < min_inliers) return false;
    // Now repeat the calculation for the best model in order to obtain the inliers and outliers set
    // This might be faster if no_iterations is large, but TEST
    // 现在重复计算最佳模型，以获得inliers和outliers集合
    // 如果no_iterations较大，这可能会更快，但是TEST
    if (inliers || outliers)
    {
        if (inliers) inliers->clear();
        if (outliers) outliers->clear();
        for (auto i : data)
        {
            if (isInlier(i, best_model.first, best_model.second, inlier_distance))
            {
                if (inliers) inliers->push_back(i);
            }
            else if (outliers) outliers->push_back(i);
        }
    }
    return true;
}

bool CostmapToLinesDBSRANSAC::linearRegression(const std::vector<KeyPoint> &data, double &slope,
        double &intercept, double *mean_x_out, double *mean_y_out)
{
    if (data.size() < 2)
    {
        ROS_ERROR("CostmapToLinesDBSRANSAC: at least 2 data points required for linear regression");
        return false;
    }
    double mean_x = 0;
    double mean_y = 0;
    for (auto i : data)
    {
        mean_x += i.x;
        mean_y += i.y;
    }
    mean_x /= double(data.size());
    mean_y /= double(data.size());
    if (mean_x_out) *mean_x_out = mean_x;
    if (mean_y_out) *mean_y_out = mean_y;
    double numerator = 0.0;
    double denominator = 0.0;
    for (auto i : data)
    {
        double dx = i.x - mean_x;
        numerator += dx * (i.y - mean_y);
        denominator += dx * dx;
    }
    if (denominator == 0)
    {
        ROS_ERROR("CostmapToLinesDBSRANSAC: linear regression failed, denominator 0");
        return false;
    }
    else slope = numerator / denominator;
    intercept = mean_y - slope * mean_x;
    return true;
}


void CostmapToLinesDBSRANSAC::reconfigureCB(CostmapToLinesDBSRANSACConfig &config, uint32_t level)
{
    boost::mutex::scoped_lock lock(parameter_mutex_);
    parameter_buffered_.max_distance_ = config.cluster_max_distance;
    parameter_buffered_.min_pts_ = config.cluster_min_pts;
    parameter_buffered_.max_pts_ = config.cluster_max_pts;
    parameter_buffered_.min_keypoint_separation_ = config.cluster_min_pts;
    ransac_inlier_distance_ = config.ransac_inlier_distance;
    ransac_min_inliers_ = config.ransac_min_inliers;
    ransac_no_iterations_ = config.ransac_no_iterations;
    ransac_remainig_outliers_ = config.ransac_remainig_outliers;
    ransac_convert_outlier_pts_ = config.ransac_convert_outlier_pts;
    ransac_filter_remaining_outlier_pts_ = config.ransac_filter_remaining_outlier_pts;
}

/*
void CostmapToLinesDBSRANSAC::adjustLineLength(const std::vector<KeyPoint>& data, const KeyPoint& linept1, const KeyPoint& linept2, 
                                           KeyPoint& line_start, KeyPoint& line_end)
{
line_start = linept1;
line_end = linept2;

// infinite line is defined by linept1 and linept2
double dir_x = line_end.x - line_start.x;
double dir_y = line_end.y - line_start.y;
double norm = std::sqrt(dir_x*dir_x + dir_y*dir_y);
dir_x /= norm;
dir_y /= norm;

// project data onto the line and check if the distance is increased in both directions
for (int i=0; i < (int) data.size(); ++i)
{
double dx = data[i].x - line_start.x;
double dy = data[i].y - line_start.y;
// check scalar product at start
double extension = dx*dir_x + dy*dir_y;
if (extension<0)
{
  line_start.x -=  dir_x*extension;
  line_start.y -=  dir_y*extension;
}
else
{
  dx = data[i].x - line_end.x;
  dy = data[i].y - line_end.y;
  // check scalar product at end
  double extension = dx*dir_x + dy*dir_y;
  if (extension>0)
  {
    line_end.x +=  dir_x*extension;
    line_end.y +=  dir_y*extension;
  }
}
}

}*/

}//end namespace costmap_converter
